Autonomous Control Strategy Research Articles

Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

Online monitoring of continuous aqueous two-phase flotation (ATPF) for the development of an autonomous control strategy

Aqueous two-phase flotation (ATPF) is a separation technique for isolating biological products such as enzymes from crude complex biosuspensions. The continuous operation mode of ATPF allows high throughput while maintaining a high separation efficiency. In this work, a laboratory plant for continuous ATPF with integrated online measurement technology is presented and ATPF experiments with the model enzyme phospholipase A2 are performed. Three electrical conductivity probes allow real time and spatially resolved characterization of phase mixing in the flotation tank caused by rising bubbles. UV/Vis spectroscopy is used to measure the enzyme concentration online at the top phase outlet. The equilibrium concentrations in the top and bottom phase are linearly dependent on the feed concentration, revealing a constant partition coefficient. The experimental results of this work provide the basis for the development of an autonomous feedback controller for the continuous ATPF process.

Towards Resiliency Enhancement of Network of Grid-Forming and Grid-Following Inverters

This paper proposes an autonomous control scheme to mitigate voltage-frequency excursions observed in a network of grid forming (GFM) and grid following (GFL) inverters in a power electronics dominated grid (PEDG). The proposed control scheme leverages GFL inverter's ability to dynamically adjust their power set-points as well as change their operation mode on-the-fly to enhance the PEDG resiliency. A supervisory controller (SC) comprising of a Droop-ΔP estimator coupled with an optimal power allocator (OPA) module dynamically adjusts the power injections from GFL inverters to maintain the power balance and restore frequency in response to disturbances. Moreover, the proposed self-ranking based coordinated mode selection (SR-CMS) algorithm dynamically reconfigures the inverter's operation mode (either GFM or GFL) to enhance the PEDG resiliency in response to events and ensure GFM inverter allocation in grid clusters. Several case studies are performed to validate the feasibility, performance, and robustness of proposed autonomous control. Finally, the proposed scheme is experimentally validated on a small-scale hardware testbed.

Modelling of Charging Demand for Electric Vehicle based on Person-trip Survey Data

In electricity distribution networks, voltage might be dropped significantly by simultaneous charging of high penetration Electric Vehicle (EV). The impact of EV on voltage drop can be different among areas depending on various factors such as original electricity demand profile without EV, EV penetration level and usage pattern, etc. For the statistical assessment of impact of high penetration EV in various distribution networks, this study proposes a model to estimate time-series data of electricity demand including EV charging. The proposed model is based on Grid Square Statistics Data on geographical distribution of population, employee, etc. and Person-trip Survey Data. As an example using the proposed model, this study compares three EV charging strategies in suburban area of Nagoya, Japan. As a result, if the EV charging starts simultaneously at 23:00 at which the night-time discount electricity rate is applied, the night-time electricity demand can be much larger than the day-time peak demand without EV. The increase can be mitigated by autonomous charging control according to the time and SOC of EV returning home in most areas. Finally, by using the calculated electricity demand profile, this study assesses the impact of EV charging on voltage profile. The result shows that the impact of EV charging on distribution network voltage can be avoided by applying the proposed autonomous charging control scheme.

Connected Autonomous Vehicle Control Strategy for Mandatory Lane-Changing At Intersection: A Cyber-Physical System Perspective

Connected Autonomous Vehicle Control Strategy for Mandatory Lane-Changing At Intersection: A Cyber-Physical System Perspective

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Autonomous Load Current Sharing Control Strategy for Distributed DC Micro-sources Based on Active Frequency Injection and Line Impedance Compensation Control

Autonomous Load Current Sharing Control Strategy for Distributed DC Micro-sources Based on Active Frequency Injection and Line Impedance Compensation Control

Online failure analysis and autonomous risk control scheme for electric buses

This paper proposes a Cyber–Physical System (CPS) scheme to address the challenge of developing self-improvement and online risk control for Electric Buses (E-Bus) in the absence or uncertainty of subsystems’ historical operational failure data. The scheme effectively deals with the unavailability or uncertainty of operational failure data by conducting an online analysis of the system’s failure through the development of a Fault Tree Analysis (FTA). Additionally, a comprehensive online risk control approach based on Reliability Centered Maintenance (RCM) is devised to define autonomous actions aimed at enhancing system uptime. The paper presents a case study on the Electric Bus drive system, establishing a link between the on-road physical driving conditions of the E-Bus, the system failure analysis, and autonomous uptime improvement actions. The implementation of the proposed approach in the case study resulted in a significant increase in the maximum allowable E-Bus mileage distance by 8603 miles while controlling the average driving cycle.

Distributed secondary optimal control for self‐maintaining microgrids on pelagic islands

AbstractConsidering the geographic distance, self‐maintaining microgrids are widely built to supply power on pelagic islands, where developing distributed autonomous control strategies while guaranteeing microgrids’ resilience and energy security have gained increasing attention. However, most existing distributed control strategies restore frequencies in an asymptotic manner, which may not meet the requirement for safe and rapid recovery after disasters. Meanwhile, self‐maintaining microgrids have relatively low percentages of fossil fuels. Power sharing among distributed energy sources (DERs) under most existing control relies on the droop coefficients, which may lead to inefficient operations. To overcome these challenges, this paper proposed a distributed control strategy for self‐maintaining microgrids. The frequency restoration time can be explicitly optimized and is robust to changes of system states, which provides faster recovery performance under disasters. The steady‐state powers of different DERs are regulated for different kinds of DERs separately and are independent of the droop coefficients, where the proper use of renewable energy under energy security can be achieved. The proposed control enhances the resilience of microgrids against disasters and meets the requirement of energy security on islands. The algorithms in the paper are verified by both simulation and experiment studies under various conditions.

Implementation of Autonomous Docking and Charging for a Supporting Robotic Fish

Underwater energy supplements for autonomous underwater vehicles (AUVs) are significant for ocean exploitation owing to energy storage and data communication limitations. Aiming at the energy supplement for bionic robotic fish, an autonomous recharging-oriented docking approach is proposed in this paper. First, an omnidirectional docking system containing a supporting robotic fish capable of visual recognition and wireless charging and a docking platform is presented. Next, five locomotive modes are designed and analyzed based on the hybrid swimming and gliding patterns for the robot fish. Utilizing the five modes, a docking procedure is planned, and an onboard-visual-based autonomous docking control strategy is presented. Aquatic experiments, including docking with the platform and refloating a small robot, are performed to verify the effectiveness of the proposed approach. The obtained results offer valuable insights into the development of autonomous docking of bionic robots, laying a solid foundation for the permanent operation of various underwater devices and robots.

An Autonomous Voltage Control Strategy for IPOS DC Power System With Power-Ripple-Based Low Bandwidth Communication

Input-parallel output-series (IPOS) power system is widely used in high output voltage and high-power applications. To improve the reliability and redundancy of the IPOS system, it is necessary to adopt a distributed and autonomous control strategy without a centralized controller. This paper investigates an autonomous voltage control strategy for IPOS DC power system. The principle and characteristics of the control strategy are studied in detail. In particular, the limitations of the control strategy in power balance performance and output voltage accuracy are analyzed. To overcome the limitations, an autonomous voltage control strategy with power-ripple-based low bandwidth communication is proposed. The power ripple on the output power line is used to share voltage compensation information among modules, which realizes voltage regulation accuracy control of the IPOS DC power system. The power-ripple-based communication is analyzed in detail. The stability of the proposed control strategy is also explored. The system has high redundancy and reliability in both control strategy and communication. The experimental prototype based on a three-module IPOS system is built to validate the proposed control and communication strategies.

Distributed Autonomous Economic Control Strategy for DC Microgrid

A distributed autonomous economic control strategy is proposed to realize the economic operation of the DC microgrid in the islanded state. The control strategy first introduces incremental cost in primary control, but due to the influence of line impedance, it is not possible to precisely achieve the consensus of incremental cost at each unit, and it generates a steady-state error. To overcome this problem, a distributed secondary control based on a dynamic consensus algorithm is designed on the basis of primary control, which makes each distributed generator operate under equal incremental cost. The two layers of control strategies cooperate work together to realize the hierarchical-distributed autonomous economic operation of the DC microgrid. Finally, the simulation model of the DC microgrid is constructed, which verifies the effectiveness of the proposed method.

Autonomous Game Control for Spacecraft Rendezvous via Adaptive Perception and Interaction

This article studies the autonomous game control problem for spacecraft rendezvous by using the adaptive perception and interaction. The rendezvous mission involves two spacecraft (named by autonomous spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> , and the other spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> , respectively), in which the control strategy of spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> is determined by only itself and cannot be intervened, but the control law of spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> needs to be designed to achieve the rendezvous under the condition that only the relative position and velocity of spacecraft in the rendezvous system are available. By formulating the rendezvous system as a two-player linear quadratic differential game (DG) with unknown intent of spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> , an autonomous game control method is proposed for spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> by using the adaptive perception and interaction. This game control approach removes the requirement for knowing the intent of spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> , relaxes the persistent excitation condition in traditional adaptive estimation methods, and guarantees the asymptotic stability of the rendezvous system. Furthermore, the intent (i.e., the weighting matrix in the cost function) of spacecraft <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> is inferred by solving a residual minimization problem. Finally, a simulation study is carried out to verify the effectiveness of the developed autonomous game control strategy.

A lumen-adapted navigation scheme with spatial awareness from monocular vision for autonomous robotic endoscopy

Lumen is a common anatomical condition in endoscopic therapy or diagnostics. The navigation of autonomous robotic endoscopy usually involves vision techniques such as detection of lumen center or anatomically specific contour features. However, these methods may fail to achieve smooth interventions and result in large conveying force without spatial awareness of the tissue state. In this paper, a novel navigation pipeline based on a spatial-aware monocular vision is proposed. The spatial awareness pipeline starts with a data-driven depth estimation technique for reconstructing real-time approximate tissue surface. The spatial shape of the lumen described by the skeleton is then extracted using digital topology. We modify the skeleton to get a smooth pathway, and design an adaptive autonomous control strategy using geometric information from the pathway. We conduct experiments on a colon phantom and ex vivo pig intestines. We test turning performance of several bending segments with different angles in phantom, as well as the overall performance of a long-range intervention task in the phantom and pig intestines. The results show our navigation scheme achieve smoother intervention with lower conveying force. The proposed navigation method with spatial awareness can effectively improve the fluency of autonomous robotic endoscopy.

The Autonomous Intersection Control Method Based on Reduction in Vehicle Conflict Relationships

Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time.

Enhancing sustainability and resilience of elderly dwellings: Optimized refurbishing parameters and air conditioning operation

Refurbishing buildings to minimize lifecycle costs and increase reliance on natural ventilation may reduce building resilience to extreme weather. This is critical for elderly whose health is affected by exposure to thermally stressful conditions. This study proposes a novel approach for refurbishing elderly houses to enhance their sustainability and heatwave resilience with the aim of supporting low-income groups. This approach involves using multi-objective optimization to identify refurbishment parameters and an autonomous control strategy to provide thermoneutral indoor conditions at a low cost.The optimization procedure and control strategy were applied to a case study for a representative apartment in the Mediterranean climate using a validated building model. The strategy led to substantial reduction (61%) in cooling energy, while the optimization yielded Pareto solutions that showed trade-offs between lifecycle cost and resilience. A selected solution resulted in reduced electrical usage for heating (37%) and cooling (45%) and decreased indoor overheating during heatwaves. The study recommends design features for cost-effective and resilient elderly housing in the Mediterranean climate, such as limited window area, enhanced thermal properties, and a modest air conditioning system for low-income populations. Larger windows and AC systems are recommended for high income populations seeking reduced operational expenses and improved sustainability.

Pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Recent accessibility of affordable sensing technologies, microcontrollers, and wireless communication technology has made it possible for stormwater systems to be retrofitted with an assortment of sensors and actuators. These smart stormwater systems have enabled the real-time sensing of their surrounding environmental dynamics, and subsequently, provide the basis for autonomous and adaptive operational control strategies. Additionally, these systems allow for inexpensive and minimally-invasive stormwater control interventions (e.g. hydraulic valve operated by cellularly-connected actuator) in lieu of new construction. However promising this area of smart stormwater control, there still remain barriers -- for experts and novices alike -- to access a set of shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical, efficient, and accessible, we present pystorms, a Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. The pystorms simulation sandbox comes with (i) a collection of real world-inspired stormwater control scenarios on which any number of control strategies can be applied and tested via (ii) an accompanying Python programming interface coupled with a stormwater simulator. For the first time, pystorms enables rigorous and efficient evaluation of smart stormwater control methodologies across a diverse set of watersheds with only a few lines of code. We present the details of pystorms here and demonstrate its usage by applying and evaluating two stormwater control strategies.

Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing

Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments; this was achieved using both pre-programmed and autonomous pH control strategies. The increased metabolic activity of the cultures, achieved via the implementation of the PAT technology, was not associated with increased LVV production. We employed a metabolic modelling strategy to elucidate the relationship between these bioprocess level events and HEK293T cell metabolism. The modelling showed that culturing of HEK293T cells in a low pH (pH 6.40) environment directly impacted the intracellular maintenance of pH and the intracellular availability of oxygen. We provide evidence that the elevated metabolic activity was a response to cope with the stress associated with low pH to maintain the favourable intracellular conditions, rather than being indicative of a superior active state of the HEK293T cell culture resulting in enhanced LVV production. Forecasting strategies were used to construct data models which identified that the novel PAT system not only had a direct relationship with process pH but also with oxygen availability; the interaction and interdependencies between these two parameters had a direct effect on the responses observed at the bioprocess level. We present data which indicate that process control and intervention using this novel refractometry-based PAT system has the potential to facilitate the fine tuning and rapid optimisation of the production environment and enable adaptive process control for enhanced process performance and robustness.

Robust Neurocontrol for Autonomous Dynamic Soaring

The flight endurance of small unmanned aerial vehicles can be significantly extended through the exploitation of naturally occurring wind phenomena. However, due to the limited computational hardware on board such aircraft and the uncertain, stochastic nature of real-world environments, there is a need for efficient and robust strategies that exhibit generalized behavior. In addressing these objectives, recent efforts have explored the use of artificial intelligence training algorithms and neural networks for the design of autonomous control schemes that exploit such wind phenomena. This study incorporates the Neuroevolution of Augmenting Topologies algorithm with domain randomization to train robust neurocontrollers that can control an aircraft along sustained traveling dynamic soaring trajectories in the presence of uncertainties and disturbances. This work presents the developed strategy for integrating robustness in neural network control systems, provides a method for quantifying and comparing robustness, and introduces an approach for identifying the network characteristics that contribute to the evolved robust behavior.

Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform

Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform